tf.pad
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Pads a tensor.
tf.pad(
tensor, paddings, mode='CONSTANT', constant_values=0, name=None
)
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
This operation pads a tensor according to the paddings you specify.
paddings is an integer tensor with shape [n, 2], where n is the rank of
tensor. For each dimension D of input, paddings[D, 0] indicates how
many values to add before the contents of tensor in that dimension, and
paddings[D, 1] indicates how many values to add after the contents of
tensor in that dimension. If mode is "REFLECT" then both paddings[D, 0]
and paddings[D, 1] must be no greater than tensor.dim_size(D) - 1. If
mode is "SYMMETRIC" then both paddings[D, 0] and paddings[D, 1] must be
no greater than tensor.dim_size(D).
The padded size of each dimension D of the output is:
paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]
For example:
t = tf.constant([[1, 2, 3], [4, 5, 6]])
paddings = tf.constant([[1, 1,], [2, 2]])
# 'constant_values' is 0.
# rank of 't' is 2.
tf.pad(t, paddings, "CONSTANT") # [[0, 0, 0, 0, 0, 0, 0],
# [0, 0, 1, 2, 3, 0, 0],
# [0, 0, 4, 5, 6, 0, 0],
# [0, 0, 0, 0, 0, 0, 0]]
tf.pad(t, paddings, "REFLECT") # [[6, 5, 4, 5, 6, 5, 4],
# [3, 2, 1, 2, 3, 2, 1],
# [6, 5, 4, 5, 6, 5, 4],
# [3, 2, 1, 2, 3, 2, 1]]
tf.pad(t, paddings, "SYMMETRIC") # [[2, 1, 1, 2, 3, 3, 2],
# [2, 1, 1, 2, 3, 3, 2],
# [5, 4, 4, 5, 6, 6, 5],
# [5, 4, 4, 5, 6, 6, 5]]
Args |
|---|
tensor
|
A Tensor.
|
paddings
|
A Tensor of type int32.
|
mode
|
One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)
|
constant_values
|
In "CONSTANT" mode, the scalar pad value to use. Must be
same type as tensor.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as tensor.
|
Raises |
|---|
ValueError
|
When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.pad\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/array_ops.py#L3163-L3219) |\n\nPads a tensor. \n\n tf.pad(\n tensor, paddings, mode='CONSTANT', constant_values=0, name=None\n )\n\n### Used in the notebooks\n\n| Used in the guide | Used in the tutorials |\n|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Advanced automatic differentiation](https://www.tensorflow.org/guide/advanced_autodiff) | - [Client-efficient large-model federated learning via \\`federated_select\\` and sparse aggregation](https://www.tensorflow.org/federated/tutorials/sparse_federated_learning) - [A Tour of TensorFlow Probability](https://www.tensorflow.org/probability/examples/A_Tour_of_TensorFlow_Probability) - [Multiple changepoint detection and Bayesian model selection](https://www.tensorflow.org/probability/examples/Multiple_changepoint_detection_and_Bayesian_model_selection) - [Graph-based Neural Structured Learning in TFX](https://www.tensorflow.org/tfx/tutorials/tfx/neural_structured_learning) |\n\nThis operation pads a `tensor` according to the `paddings` you specify.\n`paddings` is an integer tensor with shape `[n, 2]`, where n is the rank of\n`tensor`. For each dimension D of `input`, `paddings[D, 0]` indicates how\nmany values to add before the contents of `tensor` in that dimension, and\n`paddings[D, 1]` indicates how many values to add after the contents of\n`tensor` in that dimension. If `mode` is \"REFLECT\" then both `paddings[D, 0]`\nand `paddings[D, 1]` must be no greater than `tensor.dim_size(D) - 1`. If\n`mode` is \"SYMMETRIC\" then both `paddings[D, 0]` and `paddings[D, 1]` must be\nno greater than `tensor.dim_size(D)`.\n\nThe padded size of each dimension D of the output is:\n\n`paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]`\n\n#### For example:\n\n t = tf.constant([[1, 2, 3], [4, 5, 6]])\n paddings = tf.constant([[1, 1,], [2, 2]])\n # 'constant_values' is 0.\n # rank of 't' is 2.\n tf.pad(t, paddings, \"CONSTANT\") # [[0, 0, 0, 0, 0, 0, 0],\n # [0, 0, 1, 2, 3, 0, 0],\n # [0, 0, 4, 5, 6, 0, 0],\n # [0, 0, 0, 0, 0, 0, 0]]\n\n tf.pad(t, paddings, \"REFLECT\") # [[6, 5, 4, 5, 6, 5, 4],\n # [3, 2, 1, 2, 3, 2, 1],\n # [6, 5, 4, 5, 6, 5, 4],\n # [3, 2, 1, 2, 3, 2, 1]]\n\n tf.pad(t, paddings, \"SYMMETRIC\") # [[2, 1, 1, 2, 3, 3, 2],\n # [2, 1, 1, 2, 3, 3, 2],\n # [5, 4, 4, 5, 6, 6, 5],\n # [5, 4, 4, 5, 6, 6, 5]]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|---------------------------------------------------------------------------------|\n| `tensor` | A `Tensor`. |\n| `paddings` | A `Tensor` of type `int32`. |\n| `mode` | One of \"CONSTANT\", \"REFLECT\", or \"SYMMETRIC\" (case-insensitive) |\n| `constant_values` | In \"CONSTANT\" mode, the scalar pad value to use. Must be same type as `tensor`. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`. Has the same type as `tensor`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|----------------------------------------------------------------|\n| `ValueError` | When mode is not one of \"CONSTANT\", \"REFLECT\", or \"SYMMETRIC\". |\n\n\u003cbr /\u003e"]]
View source on GitHub